1. Technical Field
The present invention generally relates to the field of electronic product availability checks, and addresses computational performance issues in such availability checks.
2. Background Information
In modern industries, supply chain management has become an important tool in the planning and organization of business processes. ATP (Availability-to-Promise) checks, also known as availability checks, allow to ensure that a company can provide a requested product at a requested time in a requested quantity. Among ATP checks, there is known a product availability check, which carries out the availability check against what is referred to as the ATP quantity. In other words, the product availability check calculates the available amount of a product at a certain time. The ATP quantity is calculated from stock, planned receipts (production orders, purchase orders, planned orders and so on), and planned requirements (sales orders, deliveries, reservations and so on).
Product availability checks use ATP time series as input data. ATP time series represent incoming and outgoing movements (receipts and issues) for specific products. In an ATP time series, the receipt, requirements and stock elements are managed in aggregated (time-based) form. ATP time series are managed separately for each planning object and updated when changes are made to this planning object. Typically, aggregation occurs on a daily basis, but other periods of aggregation are conceivable, as well. ATP time series thus represent the current planning situation for the planning object for a series of points of time or time periods (buckets).
In a known solution implemented, e.g., in software packages SAP R/3 and SAP APO by SAP AG, Walidorf, and enabling a characteristics-based product availability check, the individual ATP time series includes a key consisting of category, sublocation, version and one or more valuated characteristics. This key uniquely identifies the particular ATP time series. Category refers to the particular type of stock, receipts and requirements that are to be considered in the product availability check. By choosing one or more categories, the user can define the scope of the availability check. Created production orders and released production orders are but two examples of categories. Sublocation and version correspond respectively to the storage location in a plant and the batch of the product. Characteristics may refer to features such as color, size, weight and length of the product. For more information on product availability checks and ATP time series, it is referred to Helmut Bartsch, Peter Bickenbach: “Supply Chain Management mit SAP APO—Supply-Chain-Modelle mit dem Advanced Planner & Optimizer 3.1”, SAP Press, 2nd edition, 2002, ISBN No. 3-89842-111-2, the content of which is herewith expressly incorporated by reference.
In the following, procedures of the conventionally known product availability check are discussed in more detail by way of example with reference to FIG. 4. The conventional embodiment is capable of checking the availability of a particular product against an incoming requirement (demand) to determine if the requirement can be fully or partially satisfied or must be declined (also referred to hereinafter as category-independent ATP check), or obtaining a product availability overview where the free amounts of a particular product are calculated in dependence of time and category (also referred to hereinafter as category-dependent ATP check). In both types of the ATP check, availability information is calculated on various levels such as plant level, sublocation level, version level, sublocation & version level, version & characteristics level, etc. In the category-independent ATP check, the minimum of the results on the levels is used as the total result of the check. For each level check, a separate ATP stack is filled based on the input ATP time series data. The input data is exploded with respect to sublocation, version and characteristics and stored in a linear structure with indexed access before it is filled into the stacks. The linear structure is referred to herein as exploded bucket array.
On the left-hand side of FIG. 4, three exemplary data sets 10, 12, 14 are depicted. Each of the data sets 10, 12, 14 represents an aggregated planning situation for a specific product with regard to a particular time bucket (e.g., day or shift) and category. For example, the data set 10 indicates that in a category C1, which in the example considered is stock-related, an aggregated quantity q1 of the product having version V and characteristics CK is required to be on stock at a time t1 in a sublocation S. The data set 12 indicates that with regard to a category C2, which in the example case is a receipt order, an aggregated quantity q2 of the product of version V and characteristics CK will be received in sublocation S at a time t2. Further, the data set 14 indicates that with regard to a category C3, which in the present example represents an issue order, an aggregated quantity q5 of the product has to be delivered at a time t5, regardless of the product's version, characteristics and storage location.
In the example, it is assumed that the time t1 is earlier than t2, which in turn is earlier than t5. Also, it is assumed that the sum of the quantities q1 and q2 is greater than the quantity q5, with q5 greater than q2.
The data sets 10, 12, 14 are input data to the product availability check and are also referred to herein as first data sets. Each of the data sets 10, 12, 14 constitutes, or is part of, a different ATP time series. While in the example considered, data for a single bucket only is illustrated in relation to each ATP time series, a person of ordinary skills in the art will appreciate that each ATP time series may, and typically will, contain data for a plurality of buckets so as to reflect a sequence or series of planning situations. Further, in the example considered, time series data in a single category only is input for each of stock, receipt, and issue. However, it will be readily appreciated that there may be issue-related input data sets or receipt-related input data set or stock-related input data sets in more than one category. As an example, there may be a fourth input data set (not illustrated in the drawings) in a different stock category than C1 or a different receipt category than C2 or a different issue category than C3.
The parameters: category, sublocation, version and characteristics together form a key (hereinafter also referred to as a first key) that is included in each input data set 10, 12, 14. This key uniquely identifies the corresponding ATP time series. The various parameters form elements of the key. They specify product-related conditions. As for the data set 10, the key element “category” has the value “C1”, the key element “sublocation” has the value “S”, the key element “version” has the value “V”, and the key element “characteristics” has the value “CK”. Thus, (C1, S, V, CK) is the key of the ATP time series associated with the data set 10. Similarly, (C2, S, V, CK) is the key of the ATP time series associated with the data set 12. As for the data set 14, the key element “category” has the value “C3”, and the remaining key elements “sublocation”, “version” and “characteristics” all have the value “-” indicating that no particular sublocation, version and characteristics are required for the product. Consequently, (C3, -, -, -) is the key of the ATP time series associated with the data set 14.
In the context of the present invention, the key element “category” can be viewed as a primary key element, and the other key elements “sublocation”, “version” and “characteristics” can be viewed as secondary key elements. It will be readily appreciated by one of ordinary skills in the art that the primary and secondary key elements are not limited to those described above and may include any number and type of parameters.
According to the known method of performing product availability checks, the input ATP time series data is exploded with respect to the secondary key elements sublocation, version and characteristics both for the purpose of category-dependent ATP checks and category-independent ATP checks. The exploded data is stored in a linear array referred to as exploded bucket array. Bucket arrays are created separately for issues, receipts and stock. Thus, three bucket arrays may be created, one for receipt orders, one for issue orders, and one for stock elements.
In the middle portion of FIG. 4, exemplary exploded bucket arrays 16, 18, 20 are depicted that have been created from the input data sets 10, 12, 14. Specifically, the bucket array 16 has been created from the stock-related data set 10, the bucket array 18 has been created from the receipt-related data set 12, and the bucket array 20 has been created based on the issue-related data set 14. Each bucket array 16, 18, 20 includes one or more array fields. An index is assigned to each field of each bucket array. Through the indices, the array fields and their content can be accessed. In FIG. 4, the fields of the bucket array 16 have an index I running from “1” to “8” to designate the eight fields contained in the array 16. Further, the fields of the bucket array 18 are marked by an index J likewise running from “1” to “8” owing to the same number of fields contained in the array 18. Finally, the single field of the array 20 carries an index K, which has a value “1” in the example case.
The data explosion involves generating, for each input data set, an intermediate data set for any combination of values of the secondary key elements of the respective input data set. The intermediate data sets include the same category, quantity and time as the input data set from which they are generated. For example, the data set 10 having (S, V, CK) as the values of its secondary key elements can be exploded into eight intermediate data sets. A first intermediate data set of the input data set 10 can be generated to include to have null values for all secondary key elements, i.e., (-, -, -, t1, C1, q1). The null value “-” may, e.g, be represented by a predetermined binary number containing all zeros or all ones. A second intermediate data set of the input data set 10 can be generated to include the value S for the secondary key element sublocation, resulting in the intermediate data set (S, -, -, t1, C1, q1). A third intermediate data set of the input data set 10 can be generated to include the value V for the secondary key element version, yielding (-, V, -, t1, C1, q1) as the corresponding intermediate data set. Similarly, a fourth intermediate data set of the input data set 10 can be generated as (-, -, CK, t1, C1, q1). Further intermediate data sets of the input data set 10 can be generated to include a combination of two non-null values of the secondary key elements, i.e., V and CK, S and CK, and S and V. A last intermediate data set can be generated to include all the values S, V, CK for the secondary key elements. One will easily appreciate that such last intermediate data set corresponds to the input data set 10 itself.
Thus, the secondary key elements sublocation, version and characteristics of the input data sets define a certain level, and the explosion process can be viewed as involving the generation of intermediate data sets for the same and all higher (more coarse) levels.
The eight intermediate data sets that can be generated in the above fashion from the data set 10 are stored in respective array fields in the bucket array 16. As stated earlier, the array fields of the bucket array 16 are assigned the index I to allow individual access to the information stored in the respective fields. As can be easily seen, the data set 12 can be similarly exploded into eight further intermediate data sets. Unlike the input data set 10, which specifies product stock, the input data set 12 specifies a product receipt. Therefore, the intermediate data sets created from the input data set 12 are stored in a different bucket array, i.e., the array 18.
As for the data set 14, this data set can be mapped onto a single intermediate data set only. The secondary key elements of this input data set define the uppermost level, i.e., the level of the plant itself with no consideration of the parameters sublocation, version and characteristics. As the data set 14 specifies neither stock nor a receipt, but an issue, it is exploded into yet another bucket array reserved for issue orders. In the example depicted in FIG. 4, it is the bucket array 20 that receives the single intermediate data set that can be generated from the data set 14. Of course, depending on the content and number of issue orders, the array 20 may require more than one array field to receive the exploded issue information.
In practical applications, exploded bucket arrays typically include a large number of entries and corresponding array fields.
In a subsequent step of the known method of performing product availability checks, the content of the exploded bucket arrays 16, 18 is used to generate what is referred to herein as ATP stacks. These are data structures that are created and temporarily stored for the purpose of the product availability check. In the ATP stacks, the information from the exploded bucket arrays is aggregated with respect to the categories. Separate ATP stacks are created for different levels. Each ATP stack is filled on the basis of the intermediate data sets in the arrays that belong to the same level as the respective stack. Intermediate data sets that represent issues are subtracted from intermediate data sets that represent stock or receipts. In this manner, the time-dependent free amount of the product or material at a particular level is obtained independent of the category.
On the right-hand side of FIG. 4, eight exemplary ATP stacks 22, 24, 26, 28, 30, 32, 34, 36 are depicted that have been created on the basis of the content of the exploded bucket arrays 16, 18, 20. The ATP stacks 22-36 include a key that consists of the secondary key elements, and further include quantity information in relation to a series of time buckets. For example, the key of the ATP stack 22 is (-, -, -) indicating that this stack is associated with the uppermost level. In other words, the ATP stack 22 provides time-dependent information on the free amount of the product regardless of its sublocation, version and characteristics. The key of the ATP stack 36, on the other hand, is (S, V, CK) indicating that the stack is for the lowermost level. To put it in different terms, the ATP stack 36 provides time-dependent information on the free amount of the product having version V and characteristics CK in sublocation S. The ATP stacks 24-34 provide similar information for intermediate levels.
The principles of aggregating exploded ATP time series data to fill ATP stacks are well-known to a person versed in the art and need not be described in detail herein. To give one example, filling the ATP stack 22 in FIG. 4 requires aggregating the intermediate data set indexed 1 of the bucket array 16, the intermediate data set indexed 1 of the bucket array 18 and the intermediate data set indexed 1 of the bucket array 20. The intermediate data set indexed 1 of the bucket array 16 indicates that the quantity q1 is on stock at time t1 and the intermediate data set indexed 1 of the bucket array 18 indicates that the quantity q2 will be received at time t2. However, the intermediate data set indexed 1 of the bucket array 20 indicates that the quantity q5 has to be delivered at time t5. Thus, the free (available) amount at time t1 is calculated as q1+q2−q5=q6. At time t2, the aggregated quantity at the highest (plant) level is zero.
In the known method of performing product availability checks, indices to array fields of receipt and stock bucket arrays are kept and stored in the ATP stacks. In the example of FIG. 4, both the index I to the stock bucket array 16 and the index J to the receipt bucket array 18 are kept in the ATP stacks 22-36. The indices in the ATP stacks, which can be viewed as pointers to the array fields of the exploded bucket arrays, point to those intermediate data sets of the bucket arrays that have been filled into the ATP stacks first. Through the inclusion of the indices, information about the categories is preserved in the ATP stacks. Specifically, the indices in the ATP stacks allow to disaggregate the information in the ATP stacks to recover category-dependent information. The use of the indices in the ATP stacks implies that the exploded bucket arrays 16, 18, 20 must be first completely filled before the ATP stacks 22-36 can be generated. Thus, a two-step process is performed that results eventually in the ATP stacks, with this process including the creation of the exploded bucket arrays from the input ATP time series data and then the creation of the ATP stacks from the exploded bucket arrays.
Reference is now made to FIG. 5, which illustrates the creation of a category-dependent product availability overview for the various levels according to the conventional method. To obtain such an overview, the information in the ATP stacks 22-36 is disaggregated with regard to the categories utilizing the stored indices to the fields of the bucket arrays 16, 18. The disaggregation is effected by splitting the stack quantities against the quantities indicated in the exploded bucket arrays. The particular manner in which the disaggregation is effected is well-known to those skilled in the art and need not be explained in greater detail here. It suffices to say that the disaggregation process results in a result (display) data set in relation to each category and time bucket on each level. In FIG. 5, a list of such result data sets is depicted on the right-hand side of this Figure. These result data sets, which are commonly designated 38 in FIG. 5, can be displayed, for example, on a monitor of the computer environment in which the product availability check is performed.
With the conventional method of performing product availability checks, the following problem may be encountered. In many industries, products are manufactured and sold in many different versions, sizes, colors, shapes, etc. For example, a steel producing company may manufacture rolls of band steel in a number of different lengths. The length of the roll represents a characteristic of the product. Therefore, ATP time series data have to be generated and maintained for each length. More generally, if large numbers of product characteristics and/or many product versions and/or many sublocations are involved in the product availability check, the aggregation in the ATP time series is small, resulting in a large number of input data sets to the product availability check.
In the conventional method, the exploded bucket arrays are data structures with direct access via an index. Typically, the exploded bucket arrays are sorted primarily by the secondary key elements, i.e., sublocation, version and characteristics. Building up an exploded bucket array then frequently involves the addition of new array fields in intermediate portions of the array. To keep the index consistent, the array has to be copied every time a new entry is added. The permanent copying and resizing of the array during its build-up from the input data sets results in a N*N runtime behaviour (N being the number of input data sets involved in the check). The growing direct-access array therefore requires considerable computing capacity and time. If the number of input data sets increases, the runtime may exceed beyond acceptable levels. This makes the conventional method of performing product availability checks unattractive for cases where large amounts of characteristics and/or many versions and/or many sublocations occur.
Accordingly, there is a need for systems, methods, and articles of manufacture that reduce the computing capacity and time required for performing an electronic product availability check.